Croitor Ibrahim, M., Ravikumar, N. orcid.org/0000-0003-0134-107X, Curd, A. orcid.org/0000-0002-3949-7523 et al. (3 more authors) (2024) Segmenting cardiac muscle Z-disks with deep neural networks. In: Tomaszewski, J.E. and Ward, A.D., (eds.) Proceedings of SPIE Medical Imaging 2024: Digital and Computational Pathology. SPIE Medical Imaging 2024: Digital and Computational Pathology, 18-23 Feb 2024, San Diego, USA. SPIE
Abstract
Z-disks are complex structures that delineate repeating sarcomeres in striated muscle. They play significant roles in cardiomyocytes such as providing mechanical stability for the contracting sarcomere, cell signalling and autophagy. Changes in Z-disk architecture have been associated with impaired cardiac function. Hence, there is a strong need to create tools to segment Z-disks from microscopy images, that overcome traditional limitations such as variability in image brightness and staining technique. In this study, we apply deep learning based segmentation models to extract Z-disks in images of striated muscle tissue. We leverage a novel Airyscan confocal dataset, which comprises high resolution images of Z-disks of healthy heart tissue, stained with Affimers for specific Z-disk proteins. We employed an interactive labelling tool, Ilastik to obtain ground truth segmentation masks and use the resulting data set to train and evaluate the performance of several state-of-the-art segmentation networks. On the test set, UNet++ achieves best segmentation performance for Z-disks in cardiomyocytes, with an average Dice score of 0.91 and outperforms other established segmentation methods including UNet, FPN, DeepLabv3+ and pix2pix. However, pix2pix demonstrates improved generalisation, when tested on an additional dataset of cardiomyocytes with a titin mutation. This is the first study to demonstrate that automated machine learning-based segmentation approaches may be used effectively to segment Z-disks in confocal microscopy images. Automated segmentation approaches and predicted segmentation masks could be used to derive morphological features of Z-disks (e.g. width and orientation), and subsequently, to quantify disease-related changes to cardiac microstructure.
Metadata
Item Type: | Proceedings Paper |
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Institution: | The University of Leeds |
Academic Units: | The University of Leeds > Faculty of Engineering & Physical Sciences (Leeds) > School of Computing (Leeds) > Biomedical & Health The University of Leeds > Faculty of Biological Sciences (Leeds) > School of Molecular and Cellular Biology (Leeds) > Cell Biology (Leeds) The University of Leeds > Academic Services (Leeds) > IT |
Funding Information: | Funder Grant number EPSRC (Engineering and Physical Sciences Research Council) EP/R025819/1 |
Depositing User: | Symplectic Publications |
Date Deposited: | 04 Jun 2024 09:36 |
Last Modified: | 04 Jun 2024 09:36 |
Status: | Published |
Publisher: | SPIE |
Identification Number: | 10.1117/12.3006886 |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:213094 |